Bernstein polynomial of recursive regression estimation with censored data

In this paper, we deal with the problem of the regression estimation near the edges under censoring. For this purpose, we consider a new recursive estimator based on the stochastic approximation algorithm and Bernstein polynomials of the regression function when the response random variable is subje...

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Veröffentlicht in:Stochastic models Jg. 38; H. 3; S. 462 - 487
1. Verfasser: Slaoui, Yousri
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Philadelphia Taylor & Francis 03.07.2022
Taylor & Francis Ltd
Schlagworte:
Algorithms
Asymptotic convergence
Bernstein polynomial
censored data
Censored data (mathematics)
Estimators
Mathematics
nonparametric regression estimation
Nonparametric statistics
Polynomials
Random variables
Regression
Statistics
stochastic approximation algorithm
stochastic approximation algorithm
censored data
nonparametric regression estimation
Asymptotic convergence
Bernstein polynomial
ISSN:1532-6349, 1532-4214
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Beschreibung
Zusammenfassung:In this paper, we deal with the problem of the regression estimation near the edges under censoring. For this purpose, we consider a new recursive estimator based on the stochastic approximation algorithm and Bernstein polynomials of the regression function when the response random variable is subject to random right censoring. We give the central limit theorem and the strong pointwise convergence rate for our proposed nonparametric recursive estimators under some mild conditions. Finally, we provide pointwise moderate deviation principles (MDP) for the proposed estimators. We corroborate these theoretical results through simulations as well as the analysis of a real data set.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1532-6349
1532-4214
DOI:10.1080/15326349.2022.2063335